Reconciliation of commercial measurement ratings

ABSTRACT

An example apparatus includes an advertisement determiner to identify a first plurality of respondents that received an addressable advertisement and a second plurality of respondents that received a linear advertisement based on combined program tuning data and reference advertisement data; a calculator to calculate a first average commercial minute rating for the addressable advertisement based on first duration weighted impressions associated with the first plurality of respondents and a second average commercial minute rating for the linear advertisement based on second duration weighted impressions associated with the second plurality of respondents; and a communication interface to transmit the first average commercial minute rating and the second average commercial minute rating for crediting the addressable advertisement and the linear advertisement with audience viewership metrics.

RELATED APPLICATION

This application is a continuation of International Application No.PCT/US21/17858, filed on Feb. 12, 2021, and titled “RECONCILIATION OFCOMMERCIAL MEASUREMENT RATINGS,” which claims the benefit of U.S.Provisional Patent Application No. 62/976,938, filed on Feb. 14, 2020,and titled “RECONCILIATION OF COMMERCIAL MEASUREMENT RATINGS,” both ofwhich are hereby incorporated herein by reference in their entireties.Priority to International Application No. PCT/US21/17858 and U.S.Provisional Patent Application No. 62/976,938 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to the reconciliation of commercial measurement ratings.

BACKGROUND

Audience measurement entities (AMEs), such as The Nielsen Company (US),LLC, may extrapolate audience viewership data for a total televisionviewing audience. The audience viewership data collected by an AME mayinclude viewership data for advertisements broadcasted during televisionprograms.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which theteachings of this disclosure may be implemented.

FIG. 2 is a block diagram of an example C3-C7 calculator included in theexample environment of FIG. 1 .

FIG. 3 illustrates an example of a linear advertisement and anaddressable advertisement during media viewing.

FIGS. 4A, 4B illustrate example results of an example database interfaceincluded in the example C3-C7 calculator of FIG. 2 to combine panel dataand Smart TV data to identify an addressable audience.

FIGS. 5A, 5B illustrate example results of recalculating minute-levelaudience data and reconciling a C3-C7 metric.

FIG. 6 illustrates an example ACM report based on duration weightedimpressions during the commercial minutes of a telecast withoutaccounting for linear advertisements and addressable advertisements.

FIG. 7 illustrates an example ACM report after an addressableadvertisement insertion.

FIG. 8 illustrates an example ACM report with different addressableadvertisements during different telecast minutes.

FIG. 9 illustrates example viewership for a linear advertisement and anaddressable advertisement over different telecast minutes.

FIG. 10 illustrates example viewership results at the respondent-level.

FIG. 11 illustrates an example output of an example duration weightedimpressions calculator included in the example C3-C7 calculator of FIG.2 .

FIG. 12 illustrates an example output of an example minute-levelaggregator included in the example C3-C7 calculator of FIG. 2 .

FIG. 13 illustrates an example output of an example commercial minuteratings calculator included in the example C3-C7 calculator of FIG. 2 .

FIGS. 14A, 14B illustrate example ACM reports from an example ad ratingsdeterminer of FIG. 1 .

FIG. 15 is a flowchart representative of machine readable instructionswhich may be executed to implement the data center and the C3-C7calculator 190 of FIGS. 1 and 2 .

FIG. 16 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 15 to implement the C3-C7 calculatorof FIGS. 1 and 2 .

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

As used herein, the term “media” includes any type of content and/oradvertisement delivered via any type of distribution medium. Thus, mediaincludes television programming or advertisements, radio programming oradvertisements, movies, web sites, streaming media, etc. As used herein,the term “media asset” refers to any individual, collection, orportion/piece of media of interest. For example, a media asset may be atelevision show episode, a movie, a clip, etc. Media assets can beidentified via unique media identifiers (e.g., a name of the mediaasset, a metadata tag, etc.). Media assets can be presented by any typeof media presentation method (e.g., via streaming, via live broadcast,from a physical medium, etc.).

Example methods, apparatus, and articles of manufacture disclosed hereinmonitor media presentations by media devices. Such media devices mayinclude, for example, Internet-enabled televisions, personal computers,Internet-enabled mobile handsets (e.g., a smartphone), video gameconsoles (e.g., Xbox®, PlayStation®), tablet computers (e.g., an iPad®),digital media players (e.g., a Roku® media player, a Slingbox®, etc.),etc.

In some examples, AMEs aggregate media monitoring information todetermine ownership and/or usage statistics of media devices, determinethe media presented by the media devices, determine audience ratings,determine relative rankings of usage and/or ownership of media devices,determine types of uses of media devices (e.g., whether a device is usedfor browsing the Internet, streaming media from the Internet, etc.),and/or determine other types of media device information. In examplesdisclosed herein, monitoring information includes, but is not limitedto, one or more of media identifying information (e.g.,media-identifying metadata, codes, signatures, watermarks, and/or otherinformation that may be used to identify presented media), applicationusage information (e.g., an identifier of an application, a time and/orduration of use of the application, a rating of the application, etc.),and/or user-identifying information (e.g., demographic information, auser identifier, a panelist identifier, a username, etc.), etc.

In some examples, audio watermarking is used to identify media such astelevision broadcasts, radio broadcasts, advertisements (televisionand/or radio), downloaded media, streaming media, prepackaged media,etc. Existing audio watermarking techniques identify media by embeddingone or more audio codes (e.g., one or more watermarks), such as mediaidentifying information and/or an identifier that may be mapped to mediaidentifying information, into an audio and/or video component. In someexamples, the watermark is embedded in the audio or video component sothat the watermark is hidden.

To identify watermarked media, the watermark(s) are extracted and usedto access a table of reference watermarks that are mapped to mediaidentifying information. In some examples, media monitoring companiesprovide watermarks and watermarking devices to media providers withwhich to encode their media source feeds. In some examples, if a mediaprovider provides multiple media source feeds (e.g., ESPN and ESPN 2,etc.), a media provider can provide a different watermark for each mediasource feed.

In some examples, signature matching is used to identify media. Unlikemedia monitoring techniques based on watermarks included with and/orembedded in the monitored media, fingerprint or signature-based mediamonitoring techniques generally use one or more inherent characteristicsof the monitored media during a monitoring time interval to generate asubstantially unique proxy for the media. Such a proxy is referred to asa signature or fingerprint, and can take any form (e.g., a series ofdigital values, a waveform, etc.) representative of any aspect(s) of themedia signal(s) (e.g., the audio and/or video signals forming the mediapresentation being monitored). A signature may be a series of signaturescollected in series over a time interval. A good signature is repeatablewhen processing the same media presentation, but is unique relative toother (e.g., different) presentations of other (e.g., different) media.Accordingly, the terms “fingerprint” and “signature” are usedinterchangeably herein and are defined herein to mean a proxy foridentifying media that is generated from one or more inherentcharacteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media source feeds. Various comparison criteria, such as across-correlation value, a Hamming distance, etc., can be evaluated todetermine whether a monitored signature matches a particular referencesignature. When a match between the monitored signature and a referencesignature is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that matched with the monitored signature. In someexamples, signature matching is based on sequences of signatures suchthat, when a match between a sequence of monitored signatures and asequence of reference signatures is found, the monitored media can beidentified as corresponding to the particular reference mediarepresented by the sequence of reference signatures that matched thesequence of monitored signatures. Because attributes, such as anidentifier of the media, a presentation time, a broadcast channel, etc.,are collected for the reference signature(s), these attributes may thenbe associated with the monitored media whose monitored signature matchedthe reference signature(s). Example systems for identifying media basedon codes and/or signatures are long known and were first disclosed inThomas, U.S. Pat. No. 5,481,294, which is hereby incorporated byreference in its entirety.

AMEs, such as The Nielsen Company (US), LLC, desire knowledge regardinghow users interact with media devices such as smartphones, tablets,laptops, smart televisions, etc. AMEs may also be referred to as mediamonitoring entities, audience survey entities, etc. In some examples,AMEs monitor media presentations made at the media devices to, amongother things, monitor exposure to advertisements, determineadvertisement effectiveness, etc. AMEs can provide media meters topeople (e.g., panelists) which can generate media monitoring data basedon the media exposure of those users. Such media meters can beassociated with a specific media device (e.g., a television, a mobilephone, a computer, etc.) and/or a specific person (e.g., a portablemeter, etc.).

As noted above, AMEs extrapolate ratings metrics and/or other audiencemeasurement data for a total television viewing audience from arelatively small sample of panelist households, also referred to hereinas panel homes. The panel homes may be well studied and are typicallychosen to be representative of an audience universe as a whole.

To help supplement panel data, an AME, such as The Nielsen Company (US),LLC, may reach agreements with pay-television provider companies toobtain the television tuning information derived from set top boxes,which is referred to herein, and in the industry, as return path data(RPD). Set-top box (STB) data includes all the data collected by theset-top box. STB data may include, for example, tuning events and/orcommands received by the STB (e.g., power on, power off, change channel,change input source, start presenting media, pause the presentation ofmedia, record a presentation of media, volume up/down, etc.). STB datamay additionally or alternatively include commands sent to a contentprovider by the STB (e.g., switch input sources, record a mediapresentation, delete a recorded media presentation, the time/date amedia presentation was started, the time a media presentation wascompleted, etc.), heartbeat signals, or the like. The set-top box datamay additionally or alternatively include a household identification(e.g. a household ID) and/or a STB identification (e.g. a STB ID).

Return path data includes any data receivable at a media serviceprovider (e.g., a such as a cable television service provider, asatellite television service provider, a streaming media serviceprovider, a content provider, etc.) via a return path to the serviceprovider from a media consumer site. As such, return path data includesat least a portion of the set-top box data. Return path data mayadditionally or alternatively include data from any other consumerdevice with network access capabilities (e.g., via a cellular network,the internet, other public or private networks, etc.). For example,return path data may include any or all of linear real time data from anSTB, guide user data from a guide server, click stream data, key streamdata (e.g., any click on the remote—volume, mute, etc.), interactiveactivity (such as Video On Demand) and any other data (e.g., data frommiddleware). RPD data can additionally or alternatively be from thenetwork (e.g., via Switched Digital software) and/or any cloud-baseddata (such as a remote server DVR) from the cloud.

Example methods, apparatus, systems, and articles of manufacture (e.g.,physical storage media) disclosed herein implement the reconciliation ofcommercial measurement ratings, such as the C3-C7 measurement ratingsproduced by The Nielsen Company (US), LLC. The C3-C7 metric representsthe average audience of National commercials within a given program,inclusive of three (C3) or seven (C7) days of time-shifted viewing. TheC3-C7 metric provides commercial metrics regarding the averagecommercial minute (ACM) for broadcasts of linear advertisements during aprogram. In examples disclosed herein, an ACM is the average number ofduration weighted impressions during the commercial minutes of atelecast. In some example, the C3-C7 metric is determined by calculatingthe duration weighted impressions for each commercial minute of atelecast by multiplying the number of commercial impressions during theprogram by the duration of the commercials airing in that minute. TheC3-C7 metric then sums the duration weighted impression for the entiretelecast and sums the commercial duration in seconds. The C3-C7 metricdetermines the ACM by dividing the total duration weighted impressionsby the total commercial duration.

In examples disclosed herein, a linear advertisement is an advertisementscheduled for broadcasting during a specific program to all householdstuned to that program. The C3-C7 metric is determined by the AME for thelinear broadcasts using tuning data measurements collected fromhouseholds during the period(s) of time that advertisement(s) was(were)broadcasted during a program.

Although example techniques disclosed herein are described from theperspective of reconciling C3-C7 commercial metrics that are based on anACM aggregation technique, the disclosed example techniques can be usedto reconcile other ratings metrics. For example, the disclosed exampletechniques could be used to reconcile commercial metrics that are basedon an exact commercial minute (ECM) aggregation technique.

However, the development of addressable advertisement insertiontechnology has changed the way commercial advertisements in telecastsare provided to at least some media devices in households. Householdshave experienced an increase in the use of smart televisions (Smart TVs)for presenting media. In examples disclosed herein, a Smart TV is atelevision that is able to connect to a network, such as the internet,and run applications. Smart TVs may also include technology that allowsadvertisers to push specific advertisements to targeted households.Additionally, addressable advertisement insertion technology can pushspecific advertisements to targeted households using set-top boxes(e.g., based on information conveyed by RPD from the set-top boxes),etc. In examples disclosed herein, an addressable advertisement is anadvertisement that is shown to a specific media device in a household.In examples disclosed herein, a media device selected for an addressableadvertisement will not present the linear advertisement originallyscheduled for that time period in the program.

To support addressable advertisement insertion technology, examplesdisclosed herein augment ACM computation for the C3-C7 metric toreconcile the C3-C7 metric to capture viewership of an advertisementaccurately. The addressable advertisement insertion technology allowsdifferent households to view different advertisements during the sameblock of time. An example C3-C7 metric may not differentiate betweenwhether a household audience was presented a linear advertisement or anaddressable advertisement while watching a program. Disclosed exampletechniques reconcile the C3-C7 metrics such that they include ACMviewership data for both the linear advertisements and the addressableadvertisements presented in households.

Examples disclosed herein reconcile the C3-C7 metric to differentiatethe ACM measurements for addressable advertisements and linearadvertisements. Examples disclosed herein collect program viewershipdata from household Smart TVs and integrate the program viewership datainto the measurement data collected for a national panel of households.The program viewership data collected from each Smart TV device in eachhousehold represents what program each Smart TV device was tuned to.Examples disclosed herein may collect the viewership data usingautomatic content recognition techniques based on watermarks,fingerprinting, etc. Examples disclosed herein may additionally oralternatively collect viewership data through a television set-top-boxand from RPD data. Examples disclosed herein also obtain reference datathat indicate which devices were served the linear advertisement duringa time for that program broadcast, and which devices were served anaddressable advertisement during that same time in the programbroadcast. Examples disclosed herein use both the program viewershipdata collected for the national panel and the reference data indicatingwhich devices presented which advertisement as inputs to the modifiedC3-C7 metric. Examples disclosed herein weight ACM measurements fromindividual Smart TV devices, set-top boxes, individuals in households,etc. to determine the C3-C7 metrics for a program such that the C3-C7distinguish between linear advertisements and addressableadvertisements.

Examples disclosed herein may also introduce a potential gap in theC3-C7 metric measurement when including the viewership data from SmartTVs into the measurement. In some examples, this gap accounts for databeing collected from Smart TVs in a home but not being collected fromother televisions in households with the Smart TVs. In some examples, toavoid understating the audience estimates, an additional weighting stepis used to correct for potential bias in households affected by theSmart TV data collection. In some examples, the additional weightingstep includes giving panel households that are in the same footprint asthe Smart TV households an additional weight (the weight being largerthan the weight added to the Smart TV households) that can be applied tothe tuning/viewing data from the non-Smart TV devices. Giving additionalweight to the viewing data in the panel households that have datacollected from all devices allows for closing the gap introduced byusing Smart TVs for measurement.

FIG. 1 is a block diagram of an example environment 100 in which theteachings of this disclosure may be implemented. The environment 100includes an example media device 105, an example media meter 110, anexample Smart TV device 115, an example service provider 120, exampleset top boxes (STBs) 125, an example addressable ad provider 130, anexample network 135, an example network interface 140, and an exampledata center 145. The data center 145 further includes an example meterdata analyzer 150, an example panel database 155, an example return pathdata (RPD) collector 160, an example RPD database 165, an example SmartTV data collector 170, an example Smart TV database 175, an exampleaddressable ad data collector 180, an example addressable ad database185, an example C3-C7 calculator 190, and an example ad ratingsdeterminer 195.

The example media device 105 is used to access and view different media.The example the media device 105 can be implemented with any device orcombinations of devices that are able to connect to media such as, forexample, a smart television (TV), a set-top box (STB), a game console, adigital video recorder (DVR), an Apple TV, a Roku device, YouTube TV, anAmazon fire device, other over-the-top (OTT) devices, etc., or anycombination thereof.

The example media meter 110 collects media monitoring information fromthe media device 105. In some examples, the media meter 110 isassociated with (e.g., installed on, coupled to, etc.) to the examplemedia device 105. For example, the media device 105 associated with themedia meter 110 presents media (e.g., via a display, etc.). In someexamples, the media device 105 that is associated with the media meter110 additionally or alternatively presents the media on separate mediapresentation equipment (e.g., speakers, a display, etc.). In suchexamples, the media meter 110 can have direct connections (e.g.,physical connections) to the media device 105, and/or may be connectedwirelessly (e.g., via Wi-Fi, via Bluetooth, etc.) to the media device105.

Additionally or alternatively, in some examples, the media meter 110 isa portable meter carried by one or more individual people. In theillustrated example, the media meter 110 monitors media presented to oneor more people associated with the media meter 110 and generates themonitoring data. In some examples, the monitoring data generated by themedia meter 110 can include watermarks or signatures associated with thepresented media. For example, the media meter 110 can determine awatermark (e.g., generate watermarks, extract watermarks, etc.) and/or asignature (e.g., generate signatures, extract signatures, etc.)associated with the presented media. Accordingly, the monitoring datacan include media signatures and/or media watermarks representative ofthe media monitored by the media meter 110. In some examples, the mediameter 110 provides the monitoring data to the data center 145 via theexample network 135.

The example Smart TV device 115 is a television that is able to connectto a network, such as the internet, and run applications. The exampleSmart TV device 115 may also include technology that allows advertisersto push specific advertisements to targeted households. In someexamples, the Smart TV device 115 includes technology (e.g., anautomatic content recognition (ACR) chip) for determining what media(e.g., an advertisement, television show, etc.) is presented on theSmart TV device 115. For example, the Smart TV device 115 may include anACR chip that takes a picture of what is presented on the screenperiodically (e.g., once every two second, once every ten seconds,etc.). In some such examples, the ACR chip in the Smart TV device 115uses a reference library to perform matching through imagefingerprinting (e.g., comparing a compressed screen shot of the media onthe screen to image fingerprints stored in the reference library). TheSmart TV device 115 determines what media is presented on the screen ofthe Smart TV device 115. In some examples, the Smart TV device 115provides the identified media from the image fingerprinting to the datacenter 145 via the example network 135.

In the illustrated example of FIG. 1 , the example service provider 120collects return path data from the example STBs 125 in households. Insome examples, the example STBs 125 generates data that may include, forexample, tuning events and/or commands received by the STBs 125 (e.g.,power on, power off, change channel, change input source, startpresenting media, pause the presentation of media, record a presentationof media, volume up/down, etc.). The data from the example STBs 125 mayadditionally or alternatively include commands sent to a contentprovider by the STBs 125 (e.g., such as one or more commands to switchinput sources, record a media presentation, delete a recorded mediapresentation, etc., and/or data related to one or more commands, such asthe time/date a media presentation was started, the time a mediapresentation was completed, etc.), heartbeat signals, or the like. Thedata from the STBs 125 may additionally or alternatively include ahousehold identification (e.g., a household ID) and/or a STBidentification (e.g., a STB ID). The example service provider 120collects return path data from the data of the STBs 125. The exampleservice provider 120 may include a cable television service provider, asatellite television service provider, a streaming media serviceprovider, a content provider, etc. In some examples, the return pathdata collected by the service provider 120 includes any or all of linearreal time data from an STB, guide user data from a guide server, clickstream data, key stream data (e.g., any click on the remote—volume,mute, etc.), interactive activity (such as Video On Demand) and anyother data (e.g., data from middleware). In some examples, the serviceprovider 120 provides the return path data to the data center 145 viathe example network 135.

The example addressable ad provider 130 is an advertisement providerthat provides addressable advertisements to selected households. Theexample addressable ad provider 130 pushes specific advertisements totargeted households (e.g., a household with demographic information thatindicates there is a baby in the household may be targeted to receive adiaper advertisement instead of a car advertisement). In examplesdisclosed herein, an addressable advertisement is an advertisement thatis shown to a specific media device in a household. The exampleaddressable ad provider 130 identifies the target households forspecific advertisements for different times (e.g., minutes) during atelecast. In some examples, the addressable ad provider 130 providesdata identifying households that received the different addressableadvertisements at the different times during a telecast to the datacenter 145 via the example network 135.

The example network 135 is a network used to transmit the monitoringdata, Smart TV data, return path data, and addressable advertisementdata to the example data center 145 via the network interface 140. Insome examples, the network 135 can be the Internet or any other suitableexternal network. In other examples, any other suitable means oftransmitting the monitoring data, Smart TV data, return path data, andaddressable advertisement data to the data center 145 can be used.

The example data center 145 is an execution environment used toimplement the example meter data analyzer 150, the example paneldatabase 155, the example RPD collector 160, the example RPD database165, the example Smart TV data collector 170, the example Smart TVdatabase 175, the example addressable ad data collector 180, the exampleaddressable ad database 185, the example C3-C7 calculator 190, and theexample ad ratings determiner 195. In some examples, the data center 145is associated with a AME. In some examples, the data center 145 can be aphysical processing center (e.g., a central facility of the AME, etc.).Additionally or alternatively, the data center 145 can be implementedvia a cloud service (e.g., AWS™, etc.).

In the illustrated example of FIG. 1 , the meter data analyzer 150collects, via the network interface 140 in communication with theexample network 135, the monitoring data from example media meter 110,which monitors media exposure associated with example media device 105(e.g., televisions, radios, computers, tablet devices, smart phones,etc.) in panel homes recruited by an AME. The example meter dataanalyzer 150 processes the gathered media monitoring data to detect,identify, credit, etc. respective media assets and/or portions thereof(e.g., media segments) associated with the corresponding monitoringdata. For example, the meter data analyzer 150 can compare themonitoring data to generated reference data to determine what respectivemedia assets and/or media segments are associated with the correspondingmonitoring data. In some examples, the meter data analyzer 150 can hashthe signatures included in the monitoring data. In some examples, themeter data analyzer 150 can identify the media by matching unhashedsignatures and/or hashed signatures. In some examples, the meter dataanalyzer 150 can identify media by matching watermarks included in themonitoring data to reference watermarks that are mapped to mediaidentifying information. The meter data analyzer 150 of the illustratedexample also analyzes the monitoring data to determine if a media asset,and/or particular portion(s) (e.g., segment(s)) thereof, is to becredited as a media exposure represented in the monitoring data. Theexample meter data analyzer 150 stores the identified monitoring data aspanel data (e.g., monitoring data associated with panel households)along with additional panel household information (e.g., demographicinformation, geographic location, etc.) from the media meter 110 in theexample panel database 155.

The example RPD collector 160 collects, via the network interface 140 incommunication with the example network 135, the return path data fromthe example service provider 120 for associating with the example STBs125. The RPD collector 160 stores the return path data along withadditional household information (e.g., demographic information,geographic location, etc.) from the STBs 125 in the example RPD database165.

The example Smart TV data collector 170 collects, via the networkinterface 140 in communication with the example network 135, the SmartTV data from the example Smart TV device 115 for monitoring mediaexposure associated with the example Smart TV device 115 households. TheSmart TV data collector 170 stores the Smart TV data along withadditional household information (e.g., demographic information,geographic location, etc.) from the Smart TV device 115 in the exampleSmart TV database 175.

The example addressable ad data collector 180 collects, via the networkinterface 140 in communication with the example network 135, theaddressable advertisement data from the example addressable ad provider130 for monitoring addressable advertisement exposure associated withmedia devices in target households. The addressable ad data collector180 stores the addressable advertisement data along with additionalhousehold information (e.g., demographic information, geographiclocation, etc.) for the household selected by the addressable adprovider 130 in the example addressable ad database 185.

The example C3-C7 calculator 190 obtains the panel data, return pathdata, Smart TV data, and reference advertisement data from the examplepanel database 155, the example RPD database 165, the example Smart TVdatabase 175, and the example addressable ad database 185, respectively.The C3-C7 calculator 190 combines the panel data, the return path data,the Smart TV data, and the reference advertisement data. The C3-C7calculator 190 analyzes the combined panel data, the return path data,the Smart TV data, and the reference advertisement data by identifyingdata associated with advertisement exposure (linear advertisements andaddressable advertisements), removing duplicate data, etc. The exampleC3-C7 calculator 190 identifies respondents that received addressableadvertisements and respondents that received linear advertisements fromthe combined and analyzed panel data, the return path data, the Smart TVdata, and the reference advertisement data. The example C3-C7 calculator190 calculates the average commercial minute ratings for minutes in atelecast that were addressable advertisements and linear advertisements.The example C3-C7 calculator 190 transmits the average commercial minuteratings to the example ad ratings determiner 195. The example C3-C7calculator 190 is described in further detail below in connection withFIG. 2 .

The example ad ratings determiner 195 determines ratings data and/orother audience metrics by using the average commercial minute ratingsfrom the C3-C7 calculator 190. In some examples, the ad ratingsdeterminer 195 can use the ratings data to select addressableadvertisements for respondents, modify the linear advertisements andaddressable advertisements, disable addressable advertisements fortarget respondents, etc. In some examples, the ad ratings determiner 195generates a report including data metrics regarding media exposureevents for advertisements during a telecast that may be presented tomedia providers and advertisers.

FIG. 2 is a block diagram of an example implementation of the C3-C7calculator 190 included in the example environment 100 of FIG. 1 . Theexample C3-C7 calculator 190 of FIG. 2 includes an example databaseinterface 205, an example advertisement determiner 210, an example adduration calculator 215, an example duration weighted impressionscalculator 220, an example minute-level aggregator 225, an examplecommercial minute ratings calculator 230, and an example communicationinterface 235.

The example database interface 205 obtains the panel data, return pathdata, Smart TV data, and reference advertisement data from the examplepanel database 155, the example RPD database 165, the example Smart TVdatabase 175, and the example addressable ad database 185, respectively.In some examples, the panel data collected from media devices (e.g., theexample media device 105 of FIG. 1 ), the return path data collectedfrom service providers (e.g., the example service provider 120 of FIG. 1), and the Smart TV data collected from Smart TV devices (e.g., theexample Smart TV device 115 of FIG. 1 ) are referred to as programtuning data of households. The database interface 205 combines the paneldata, the return path data, the Smart TV data, and the referenceadvertisement data. The database interface 205 analyzes the combinedpanel data, the return path data, the Smart TV data, and the referenceadvertisement data by identifying data associated with advertisementexposure (linear advertisements and addressable advertisements),removing duplicate data, etc.

The example advertisement determiner 210 identifies respondents thatreceived addressable advertisements and respondents that receive linearadvertisements. In examples disclosed herein, a respondent may include ahousehold, an individual person, an individual media device, etc. Theadvertisement determiner 210 identifies the respondents that receivedaddressable advertisements and the respondents that receive linearadvertisements from the combined program tuning data and the referenceadvertisement data from the example database interface 205.

The example ad duration calculator 215 calculates advertisementdurations at the respondent-level. The ad duration calculator 215calculates the durations for linear advertisements and for addressableadvertisements. In some examples, the ad duration calculator 215determines the durations of a linear advertisement and an addressableadvertisement in seconds for each minute in a telecast. For example,during one minute of a telecast, the ad duration calculator 215determines that a linear advertisement was presented for 45 secondsduring the minute and an addressable advertisement was presented for 15seconds during the minute.

The example duration weighted impressions calculator 220 calculatesduration weighted impressions at the respondent-level based on thedurations of linear advertisements and the durations of addressableadvertisements calculated by the ad duration calculator 215. Theduration weighted impressions calculator 220 calculates the durationweighted impressions for the respondents. In examples disclosed herein,impressions are the sums of weights of individual respondents thatviewed the advertisements during a given minute. In some examples, theduration weighted impressions calculator 220 calculates the durationweighted impressions for the linear advertisements and addressableadvertisements using Equations 1a and 1b below.duration weighted linear impressions=weight*linear duration  (Equation1a)duration weighted addressable impressions=weight*addressableduration  (Equation 1b)As illustrated in Equations 1a, 1b above, the duration weightedimpressions account for weight values associated with each respondentand the durations of the linear advertisements and addressableadvertisements calculated by the ad duration calculator 215. In suchexamples, the duration weighted impressions include weighting valuesfrom the respondents to correct for potential bias in householdsaffected by the Smart TV data collection. In some examples, theadditional weighting step includes giving panel households that are inthe same footprint as the Smart TV households an additional weight (theweight being larger than the weight added to the Smart TV households)that can be applied to the tuning/viewing data from the non-Smart TVdevices of those panel households. Giving additional weight to theviewing data in the panel households that have data collected from alldevices allows for closing the gap introduced by using Smart TVs formeasurement.

The example minute-level aggregator 225 aggregates the duration weightedimpressions from the duration weighted impressions calculator 220 to theminute-level. The minute-level aggregator 225 aggregates the durationweighted impressions for linear advertisements and addressableadvertisements for the respondents for each commercial minute in thetelecast. The minute-level aggregator 225 sums the duration weightedimpressions for each commercial minute using the below Equations 2a and2b.total duration wtd linear impressions=Σ(weight*linearduration)  (Equation 2a)total duration wtd addressable impressions=Σ(weight*addressableduration)  (Equation 2b)The minute-level aggregator 225 determines the total commercial durationfor the telecast in seconds (e.g., across all commercial minutes). Theminute-level aggregator 225 determines the minute-level impressionsacross the telecast for both the linear advertisements and theaddressable advertisements by dividing the resulting total durationweighted impressions from Equations 2a, 2b above by the total commercialduration for the telecast.

The example commercial minute ratings calculator 230 calculates theaverage commercial minute ratings based on the minute-level durationweighted impressions from the example minute-level aggregator 225. Thecommercial minute ratings calculator 230 calculates the averagecommercial minute rating for the addressable advertisement and theaverage commercial minute rating for the linear advertisement using thetotal duration weighted impressions of the addressable advertisement,the total duration weighted impressions of the linear advertisement, anda total number of commercial seconds from the example minute-levelaggregator 225. The example commercial minute ratings calculator 230 maycalculate the average commercial minute ratings using Equations 3a and3b below.

$\begin{matrix}{{{average}{linear}{commercial}{minute}{rating}} = \frac{{total}{duration}{wtd}{}{linear}{impressions}}{{total}{commercial}{duration}}} & \left( {{Equation}3a} \right)\end{matrix}$ $\begin{matrix}{{{average}{addressable}{commercial}{minute}{rating}} = \frac{{total}{duration}{wtd}{addressable}{impressions}}{{total}{commercial}{duration}}} & \left( {{Equation}3b} \right)\end{matrix}$

In Equations 3a, 3b above, the total commercial duration represents thesum of the total duration of the linear advertisement in seconds and thetotal duration of the addressable advertisement in seconds. Thecommercial minute ratings calculator 230 determines the averagecommercial minute ratings while accounting for different advertisementdurations at the respondent-level to depict changes in an audience forlinear advertisements and addressable advertisements in a telecast.

The example communication interface 235 transmits the average commercialminute ratings (e.g., the average linear commercial minute ratings andthe average addressable commercial minute ratings) from the commercialminute ratings calculator 230 to the example ad ratings determiner 195of FIG. 1 . The example communication interface 235 transmits theaverage commercial minute ratings for the linear advertisements andaddressable advertisements to the ad ratings determiner 195 to creditthe addressable advertisement and the linear advertisement with audienceviewership metrics.

FIG. 3 illustrates an example of a linear advertisement and anaddressable advertisement during an example media viewing 300. Theexample media viewing 300 includes an example first program 302, anexample addressable advertisement period 304, and an example secondprogram 306. The example addressable advertisement period 304 includesan example first time period 308 and an example second time period 310.The example of FIG. 3 illustrates an example of two minutes of viewing.In the illustrated example, the user views media on the example firstprogram 302 (e.g., ESPN) for a first minute. During the first program302, there is a linear advertisement for 30 seconds followed by anaddressable advertisement period 304. In the illustrated example, theuser views the addressable advertisement period 304 for the first timeperiod 308 (e.g., five seconds) on the first program 302. After thefirst time period 308, the user tunes to the second program 306 (e.g.,TNT). In the illustrated example, the first time period 308 (e.g., fiveseconds) is credited to the addressable advertisement on the firstprogram 302. The second time period 310 is credited towards the linearcommercial total on the first program 302 instead of credited as viewingthe second program 306 because if the viewer is not viewing theaddressable advertisement during the second time period 310, then theC3-C7 metric assumes the second time period 310 should be credited tothe linear advertisement on the first program 302.

FIGS. 4A, 4B illustrate example results 400, 410 of the example databaseinterface 205 included in the example C3-C7 calculator 190 of FIG. 2combining panel data and Smart TV data and identifying an addressableaudience. The example results 400 of FIG. 4A illustrate the exampledatabase interface 205 combining the panel data, return path data, andthe Smart TV data. The example results 400 include example C3 ratings402, 404, 406 for a telecast before accounting for addressableadvertisements. In the illustrated example, the results 400 include C3ratings for the telecast based on the data obtained by the databaseinterface 205. In the illustrated example, the database interface 205obtains panel data for the C3 rating 402. The database interface 205obtains panel data and Smart TV data and combines the data for the C3rating 404. The database interface 205 obtains Smart TV data for the C3rating 406.

FIG. 4B illustrates the results 410 for an addressable advertisementaudience from reference advertisement data. The results 410 includeexample universe device impressions 412, example target impressions 414,and example reportable impressions 416. The example database interface205 obtains the reference advertisement data to identify the universedevice impressions 412, example target impressions 414, and examplereportable impressions 416. In examples disclosed herein, not alldevices (e.g., Smart TV device, other media devices, etc.) in theuniverse device impression 412 meet a desired target demographic for anaddressable advertisement or are not able to receive addressableadvertisements (e.g., lack of technology). The target impression 414represents the number of impressions based on targeted households for anaddressable advertisement. However, not all targeted households see theaddressable advertisement (e.g., change of the channel, etc.). Thereportable impressions 416 illustrate the number of target householdwith reportable impressions for viewing the addressable advertisement.The example database interface 205 obtains the panel data, return pathdata, and Smart TV data illustrated in the example results 400 and thereference addressable advertisement data illustrated in the exampleresults 410 to adjust the C3-C7 metric for addressable advertisementimpressions.

FIGS. 5A, 5B illustrate example results 500, 530 of recalculatingminute-level audience data and reconciling C3-C7 metric. The exampleresults 500 of FIG. 5A illustrate recalculating minute-level audienceimpressions based on the results 400 and 410 of FIGS. 4A, 4B. Theexample results 500 include example advertisements 502-510, examplecommercial minutes 512, example advertisement brands 514, exampleminute-level impressions 516, example addressable impressions 518, andexample linear impressions 520. In the illustrated example, theaddressable impressions 518 and the linear impressions 520 for theadvertisements 502-510 are determined from the minute-level impressions516.

The example results 530 of FIG. 5B illustrate reconciling the C3-C7metric to calculate ACM ratings based on the minute-level audienceimpressions from the example results 500 of FIG. 5A. The example results530 include an example telecast 532, an example program 534, an examplenetwork 536, an example prior C3 impression 538, an example prior C3impression 540, an example reconciled C3 impression 542, and an exampledelta C3 metric 544. In the illustrated example, the prior C3 impression538 illustrates the C3 impressions that do not differentiate betweenlinear advertisement impressions and addressable advertisementimpressions (e.g., the addressable impressions 518 and the linearimpressions 520) based on panel data. In the illustrated example, theprior C3 impression 540 illustrates the C3 impressions that do notdifferentiate between linear advertisement impressions and addressableadvertisement impressions (e.g., the addressable impressions 518 and thelinear impressions 520) based on panel data, return path data, and SmartTV data. The reconciled C3 impression 542 illustrates the C3 impressionsthat different between linear advertisement impressions and addressableadvertisement impressions (e.g., the addressable impressions 518 and thelinear impressions 520) according to the teachings of this disclosure.In the illustrated example, the delta C3 metric 544 illustrates thedifference between the prior C3 impression 540 and the reconciled C3impressions 542 at the respondent level based on the difference betweenthe linear advertisement impressions and addressable advertisementimpressions. Although the delta C3 metric 544 is the difference betweenthe C3 model calculating impressions when not differentiating linearimpressions and addressable impressions (e.g., prior C3 impression 540)and when differentiating linear impressions and addressable impressions(e.g., the reconciled C3 impressions 542), the delta C3 metric 544 doesnot illustrate the number of addressable impressions.

FIG. 6 illustrates example results for an ACM report 600 based onduration weighted impressions during the commercial minutes of atelecast without accounting for linear advertisements and addressableadvertisements. In the illustrated example, the C3-C7 commercial metricsreport the ACM which is the average number of duration weightedimpressions during the commercial minutes of a telecast. Therefore,every commercial during the same telecast will have the same ACM rating.The example ACM report 600 includes example program name 602, exampletelecast start time 604, example minutes of telecast 606, examplecommercial durations 608, example impressions 610, and example durationweighted impressions 612 for each of example commercial minutes 614,616, 618. The ACM report 600 illustrates a ten minute long telecast thathas commercials during the minutes 4-6 (e.g., commercial minutes 614,616, 618). In the illustrated example, the impressions 610 of thecommercial minutes 614, 616, 618 are duration weighted by multiplyingthe impressions 610 by the commercial duration 608. For example, forcommercial minute 614, the impressions 610 (e.g., 25,034) is multipliedby the commercial duration 608 (e.g., 24) to determine the durationweighted impression 612 (e.g., 600,816). In the illustrated example, theduration weighted impressions 612 do not differentiate impressions forlinear advertisements and addressable advertisements for the commercialminutes 614, 616, 618. In the illustrated example, the ACM rating forthe telecast is found by determining the total duration weightedimpression for the telecast (e.g., 2,799,306) and determining the totalfor the commercial duration 608 (e.g., 134). The ACM rating from the ACMreport 600 is the total duration weighted impressions divided by thetotal commercial duration (e.g., 2,799,306/134=20,890). The ACM ratingfor the ACM report 600 is the same for all commercials during thecommercial minutes 614, 616, 618 (e.g., no differentiation betweenlinear advertisements and addressable advertisements).

FIG. 7 illustrates example results for an ACM report 700 after anaddressable advertisement insertion. The example ACM report 700 includesexample program names 702, example telecast start times 704, examplecommercial durations 708, example impressions 710, and example durationweighted impressions for an example linear advertisement 714 and anexample addressable advertisement 716. The ACM report 700 illustratesthe addressable advertisement 716 that is inserted in minute five of thetelecast to some target households. In the illustrated example, the ACMcomputations illustrated in the example of FIG. 6 are not applicablewhen an addressable advertisement (e.g., the addressable advertisement716) is inserted into a commercial minute of the telecast becausedifferent households will see different advertisements of differingdurations at different time of the commercial minutes (and, thus, allcommercials do not have the same ACM rating as illustrated in FIG. 6 ).The ACM report 700 determines the duration weighted impressions 712 forthe linear advertisement 714 and the addressable advertisement 716separately to determine different ACM ratings. In the illustratedexample, the ACM rating for the linear advertisements only is determinedto be 18,070 by dividing the total linear impressions 710 (e.g.,excluding the impressions 710 for the addressable advertisement 716) bythe total commercial duration 708 (e.g., 2,421,426/134=18,070). The ACMrating of 18,070 for the linear advertisements of the ACM report 700 isdifferent from the prior ACM ratings determined in the ACM report 600(20,890). FIG. 7 illustrates that the ACM ratings calculations from FIG.6 are not applicable when an addressable advertisement is inserted inthe telecast. In some examples, if the linear advertisement 714 and theaddressable advertisement 716 are not the same length/duration (e.g.,the addressable advertisement 716 has a commercial duration 708 of 15seconds), there are remaining seconds (e.g., 45 seconds) of linearadvertisements that would be eligible for the C3 metric. However, theremaining seconds (e.g., the 45 seconds in the illustrated example)would not be accounted for based on the ACM calculations illustrated inFIGS. 6 and 7 .

FIG. 8 illustrates example results for an ACM report 800 with differentaddressable advertisements during different telecast minutes. Theexample ACM report 800 includes example program names 802, exampletelecast start times 804, example telecast minutes 806, examplecommercial durations 808, example impression 810, and example durationweighted impressions 812 for the example linear advertisement 814, theexample addressable advertisement 816, the example addressableadvertisement 818, and the example combination addressable advertisement820 for minute 5 and the example addressable advertisement 822, theexample addressable advertisement 824, and the example linearadvertisement 826 for minute 6. In the illustrated example, the ACMcomputations illustrated in the example of FIG. 6 are not applicablewhen multiple addressable advertisements inserted into a commercialminute of the telecast. The ACM report 800 illustrates how during acommercial minute (e.g., minute 5), some households may see the linearadvertisement 814, some households may see the addressable advertisement816, some households may see the addressable advertisement 816, and somehouseholds may see a combination addressable advertisement 820 (e.g., aportion of the addressable advertisement 816 and a portion of theaddressable advertisement 818). FIG. 8 illustrates that the ACM ratingscalculations from FIG. 6 are not applicable when multiple addressableadvertisement and/or combinations of addressable advertisements areinserted in the telecast. Simply calculating an ACM rating for allcommercials during the telecast in the ACM report 800 would not accountfor the different impressions of the linear advertisement 814, theaddressable advertisement 816, the addressable advertisement 818, andthe combination addressable advertisement 820 for minute 5 and theaddressable advertisement 822, the addressable advertisement 824, andthe linear advertisement 826 for minute 6. Examples disclosed hereinreconcile the C3-C7 metric to determine the ACM ratings for the telecastthat account for differences in linear advertisements and addressableadvertisement.

FIG. 9 illustrates example viewership for the linear advertisement andaddressable advertisement over different telecast minutes in accordancewith the teachings of this disclosure. An example Table 900 of FIG. 9includes example program names 902, example telecast start times 904,example telecast minutes 906, example commercial durations 908, exampleimpressions 910, and example duration weighted impressions 912 for theexample advertisements 914-920. In the illustrated example, duringminute 5 of the telecast minutes 906, the advertisement 914 illustrateshouseholds exposed to a linear advertisement, the advertisement 916illustrates households exposed to 45 seconds of the linear advertisementand 15 seconds of a first addressable advertisement, the advertisement918 illustrates households exposed to 40 seconds of the linearadvertisement and 20 seconds of a second addressable advertisement, andthe advertisement 920 illustrates household exposed to 25 seconds of thelinear advertisement and 35 seconds of the first addressableadvertisement and the second addressable advertisement. Examplesdisclosed herein calculate ACM ratings to account for differentdurations and exposures to linear advertisement and addressableadvertisement combinations. In the illustrated example, the impressions910 are the sums of weights of individual respondents (households orpersons) that viewed the advertisement (e.g., the linear advertisementor addressable advertisement) during a given telecast minute 906. Theduration weighted impressions 912 at the respondent level are able toadjust for different advertisement exposures and durations as seen inthe example Table 900.

FIG. 10 illustrates example viewership results 1000 at therespondent-level. The example viewership results 1000 includes theexample telecast minutes 906, the example commercial durations 908, andthe example impressions 910 for the example advertisements 914-920 ofFIG. 9 . The viewership results 1000 further includes examplerespondents 1002, example respondent weights 1004, example telecastminutes 1006, and example total commercial durations 1008 based on thetelecast minutes 906, the commercial durations 908, and the impressions910 for the advertisements 914-920. The viewership results 1000illustrate the different weights for respondents during a telecast. Theviewership results 1000 include example total respondent weight 1020(21,014), which is the sum of the respondent weights during the telecastfor minute 5 of the telecast minutes 1006 (the same minute as theadvertisements 914-920). The example respondent weights 1004 may be usedby the example duration weighted impressions calculator 220 of FIG. 2 todetermine the duration weighted impressions for the advertisements914-920, which is described in further detail below in connection withFIG. 11 .

FIG. 11 illustrates example results 1100 of the example durationweighted impressions calculator 220 included in the example C3-C7calculator 190 of FIG. 2 . The example results 1100 include the exampletelecast minutes 906, the example commercial durations 908, and theexample impressions 910 for the example advertisements 914-920 of FIG. 9and the example respondents 1002, the example respondent weights 1004,and the example telecast minutes 1006 of FIG. 10 . The results 1100further include example linear durations 1102, example addressabledurations 1104, example duration weighted linear impressions 1106, andexample duration weighted addressable impressions 1108. The exampleduration weighted impressions calculator 220 calculates the durationweighted linear impressions 1106 and the duration weighted addressableimpressions 1108 using Equations 1a, 1b, as described above inconnection with FIG. 2 . The example duration weighted impressionscalculator 220 calculates the duration weighted linear impressions 1106and the duration weighted addressable impressions 1108 using the lineardurations 1102 and the addressable durations 1104 from the example adduration calculator 215 of FIG. 2 and the respondent weights 1004.

FIG. 12 illustrates example results 1200 of the example minute-levelaggregator 225 included in the example C3-C7 calculator 190 of FIG. 2 .The example results 1200 include the example telecast minutes 906, theexample commercial durations 908, and the example impressions 910 forthe example advertisements 914-920 of FIG. 9 and for exampleadvertisements 1202, 1204. The results 1200 further include exampletelecast minutes 1206, example total weight sums 1208, example totalcommercial durations 1210, example duration weighted linear impressions1212, and example duration weighted addressable impressions 1214. Theresults 1200 illustrate the minute-level aggregator 225 aggregating therespondent-level results 1100 of FIG. 11 from the example durationweighted impressions calculator 220. The example minute-level aggregator225 sums the duration weighted impressions (e.g., the example durationweighted linear impressions 1106 and the example duration weightedaddressable impressions 1108 of FIG. 11 ) across each commercial minuteusing the Equations 2a, 2b, as described above in connection with FIG. 2, to determine the duration weighted linear impressions 1212 and theduration weighted addressable impressions 1214. The example results 1200includes an example total telecast commercial duration 1216, an exampletotal duration weighted linear impression 1218, and an example totalduration weighted addressable impression 1220. The minute-levelaggregator 225 determines the total telecast commercial duration 1216 tobe 134 seconds, the total duration weighted linear impression 1218 to be2,503,576, and the total duration weighted addressable impression 1220to be 295,73. The total telecast commercial duration 1216, the totalduration weighted linear impression 1218, and the total durationweighted addressable impression 1220 are used by the example commercialminute ratings calculator 230 of FIG. 2 , as described in further detailbelow in connection with FIG. 13 .

FIG. 13 illustrates example results 1300 of the example commercialminute ratings calculator 230 included in the example C3-C7 calculator190 of FIG. 2 . The example results 1300 include the example telecastminutes 906, the example commercial durations 908, and the exampleimpressions 910 for the example advertisements 914-920 of FIG. 9 and forexample advertisements 1202, 1204 of FIG. 12 . The results 1300 furtherinclude example telecast minutes 1206, example total weight sums 1208,example total commercial durations 1210, example duration weightedlinear impressions 1212, and example duration weighted addressableimpressions 1214 of FIG. 12 , example linear commercial minute ratings1302, and example addressable commercial minute ratings 1304. Thecommercial minute ratings calculator 230 determines the linearcommercial minute ratings 1302 and the addressable commercial minuteratings 1304 using the Equations 3a, 3b, as described in detail above inconnection with FIG. 2 . In some examples, when a minute in the telecastminutes 1206 has only duration weighted linear impressions 1212 (e.g.,the duration weighted addressable impressions is 0), the linearcommercial minute ratings 1302 are equivalent to summing the respondentweights. The example commercial minute ratings calculator 230 determinestotal linear commercial minute ratings and total addressable commercialminute ratings using the total telecast commercial duration 1216, thetotal duration weighted linear impression 1218, and the total durationweighted addressable impression 1220 of FIG. 12 from the exampleminute-level aggregator 225 of FIG. 2 . For examples, the total linearcommercial minute ratings is determined to be 18,683 (e.g.,2,503,576/134=18,683) and the total addressable commercial minuteratings is determined to be 2,207 (e.g., 295,730/134=2,207). The totallinear commercial minute ratings and total addressable commercial minuteratings from the results 1300 illustrate ACM ratings for differentadvertisements (linear advertisements and addressable advertisements)that may also have different advertisement durations, which improve thedepiction of changes in the audience for different advertisements.

FIGS. 14A, 14B illustrate example ACM reports 1400, 1410 from the adratings determiner 195 of FIG. 1 . The example ACM report 1400 of FIG.14A illustrates an example report for the reconciled C3 metric based onthe results of the C3-C7 calculator 190 of FIG. 1 . The example ACMreport 1400 includes example C3 currency average audience estimates1402, example adjusted C3 average audience estimates 1404, exampletargeted impressions 1406, and example reconciled C3 average audienceestimates 1408. The example C3 currency average audience estimates 1402illustrates the average audience estimates for a program using the priorC3 calculation using only panel data to determine currency estimates.The example adjusted C3 average audience estimates 1404 illustrates theaverage audience estimates for a program using an adjusted C3calculation with panel data and ACR data. The example targetedimpressions 1406 illustrates a total target number of impressions foraverage audience watching the program (how many target individuals fromthe audience being measured that are targeted for watching the programduring the duration). The example reconciled C3 average audienceestimates 1408 illustrates the average audience estimates for a programusing the reconciled C3 calculation from the results of the C3-C7calculator 190 of FIG. 1 . In the illustrated example, each of the C3currency average audience estimates 1402, the adjusted C3 averageaudience estimates 1404, and the reconciled C3 average audienceestimates 1408 include “AA %” columns that represent average audiencepercentage (the percent of the total population being measured that waswatching the program) and “AA Projection” columns that represent theaverage audience watching the program represented in terms ofimpressions (how many actual individuals from the audience beingmeasured were watching the program on average during the duration).

The example ACM report 1410 of FIG. 14B illustrates an example reportfor the reconciled minute based on the results of the C3-C7 calculator190. The example ACM report 1410 includes example C3 currency averageaudience estimates 1412, example adjusted C3 average audience estimates1414, example targeted impressions 1416, and example reconciled C3average audience estimates 1418. The example C3 currency averageaudience estimates 1412 illustrates the average audience estimates forprogram minutes using the prior C3 calculation using only panel data todetermine currency estimates. The example adjusted C3 average audienceestimates 1414 illustrates the average audience estimates for programminutes using an adjusted C3 calculation with panel data and ACR data.The example targeted impressions 1416 illustrates total target number ofimpressions for average audience watching program minutes (how manytarget individuals from the audience being measured that are targetedfor watching the program minute). The example reconciled C3 averageaudience estimates 1418 illustrates the average audience estimates forprogram minutes using the reconciled C3 calculation from the results ofthe C3-C7 calculator 190 of FIG. 1 . In the illustrated example, each ofthe C3 currency average audience estimates 1412, the adjusted C3 averageaudience estimates 1414, and the reconciled C3 average audienceestimates 1418 include “AA %” columns that represent average audiencepercentage (the percent of the total population being measured that waswatching the program minute) and “AA Projection” columns that representthe average audience watching the program represented in terms ofimpressions (how many actual individuals from the audience beingmeasured were watching the program minute on average).

While example manners of implementing the data center 145 and the C3-C7calculator 190 are illustrated in FIG. 1 and FIG. 2 , one or more of theelements, processes and/or devices illustrated in FIGS. 1 and 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example meter data analyzer 150, theexample panel database 155, the example RPD collector 160, the exampleRPD database 165, the example Smart TV data collector 170, the exampleSmart TV database 175, the example addressable ad data collector 180,the example addressable ad database 185, the example ad ratingsdeterminer 195, the example database interface 205, the exampleadvertisement determiner 210, the example ad duration calculator 215,the example duration weighted impressions calculator 220, the exampleminute-level aggregator 225, the example commercial minute ratingscalculator 230, the example communication interface 235 and/or, moregenerally, the example data center 145 and the example C3-C7 calculator190 of FIGS. 1 and 2 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example meter data analyzer 150, the example paneldatabase 155, the example RPD collector 160, the example RPD database165, the example Smart TV data collector 170, the example Smart TVdatabase 175, the example addressable ad data collector 180, the exampleaddressable ad database 185, the example ad ratings determiner 195, theexample database interface 205, the example advertisement determiner210, the example ad duration calculator 215, the example durationweighted impressions calculator 220, the example minute-level aggregator225, the example commercial minute ratings calculator 230, the examplecommunication interface 235 and/or, more generally, the example datacenter 145 and the example C3-C7 calculator 190 of FIGS. 1 and 2 couldbe implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example meter data analyzer 150, theexample panel database 155, the example RPD collector 160, the exampleRPD database 165, the example Smart TV data collector 170, the exampleSmart TV database 175, the example addressable ad data collector 180,the example addressable ad database 185, the example ad ratingsdeterminer 195, the example database interface 205, the exampleadvertisement determiner 210, the example ad duration calculator 215,the example duration weighted impressions calculator 220, the exampleminute-level aggregator 225, the example commercial minute ratingscalculator 230, the example communication interface 235 is/are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example data center 145 and the exampleC3-C7 calculator 190 of FIGS. 1 and 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and 2 , and/or may include more than one of anyor all of the illustrated elements, processes and devices. As usedherein, the phrase “in communication,” including variations thereof,encompasses direct communication and/or indirect communication throughone or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the data center 145 and the C3-C7calculator 190 of FIGS. 1 and 2 is shown in FIG. 15 . The machinereadable instructions may be one or more executable programs orportion(s) of an executable program for execution by a computerprocessor and/or processor circuitry, such as the processor 1612 shownin the example processor platform 1600 discussed below in connectionwith FIG. 16 . The program may be embodied in software stored on anon-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 1612, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1612 and/or embodied in firmware or dedicated hardware. Further,although the example program is described with reference to theflowchart illustrated in FIG. 15 , many other methods of implementingthe example data center 145 and the example C3-C7 calculator 190 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware. The processor circuitry may bedistributed in different network locations and/or local to one or moredevices (e.g., a multi-core processor in a single machine, multipleprocessors distributed across a server rack, etc.).

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as dataor a data structure (e.g., portions of instructions, code,representations of code, etc.) that may be utilized to create,manufacture, and/or produce machine executable instructions. Forexample, the machine readable instructions may be fragmented and storedon one or more storage devices and/or computing devices (e.g., servers)located at the same or different locations of a network or collection ofnetworks (e.g., in the cloud, in edge devices, etc.). The machinereadable instructions may require one or more of installation,modification, adaptation, updating, combining, supplementing,configuring, decryption, decompression, unpacking, distribution,reassignment, compilation, etc. in order to make them directly readable,interpretable, and/or executable by a computing device and/or othermachine. For example, the machine readable instructions may be stored inmultiple parts, which are individually compressed, encrypted, and storedon separate computing devices, wherein the parts when decrypted,decompressed, and combined form a set of executable instructions thatimplement one or more functions that may together form a program such asthat described herein.

In another example, the machine readable instructions may be stored in astate in which they may be read by processor circuitry, but requireaddition of a library (e.g., a dynamic link library (DLL)), a softwaredevelopment kit (SDK), an application programming interface (API), etc.in order to execute the instructions on a particular computing device orother device. In another example, the machine readable instructions mayneed to be configured (e.g., settings stored, data input, networkaddresses recorded, etc.) before the machine readable instructionsand/or the corresponding program(s) can be executed in whole or in part.Thus, machine readable media, as used herein, may include machinereadable instructions and/or program(s) regardless of the particularformat or state of the machine readable instructions and/or program(s)when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example process of FIG. 15 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIG. 15 is a flowchart representative of machine readable instructionswhich may be executed to implement the example data center 145 and theexample C3-C7 calculator 190 of FIGS. 1 and 2 . The program 1500 of FIG.15 begins execution at block 1505 at which the example databaseinterface 205 obtains panel data, RPD data, and Smart TV data. In someexamples, the example meter data analyzer 150 determines the panel data.The example meter data analyzer 150 collects monitoring data from theexample media meter 110, which monitors media exposure associated withexample media device 105 (e.g., televisions, radios, computers, tabletdevices, smart phones, etc.) in panel homes recruited by an AME. Theexample meter data analyzer 150 processes the gathered media monitoringdata to detect, identify, credit, etc. respective media assets and/orportions thereof (e.g., media segments) associated with thecorresponding monitoring data. The example meter data analyzer 150stores the identified monitoring data as panel data (e.g., monitoringdata associated with panel households) along with additional panelhousehold information (e.g., demographic information, geographiclocation, etc.) from the media meter 110 in the example panel database155. In some examples, the example RPD collector 160 determines thereturn path data. The example RPD collector 160 collects the return pathdata from the example service provider 120 associated with the STBs 125.The RPD collector 160 stores the return path data along with additionalhousehold information (e.g., demographic information, geographiclocation, etc.) from the STBs 125 in the example RPD database 165. Insome examples, the example Smart TV data collector 170 determines theSmart TV data. The example Smart TV data collector 170 collects theSmart TV data from the example Smart TV device 115 for monitoring mediaexposure associated with the example Smart TV device 115 households. TheSmart TV data collector 170 stores the Smart TV data along withadditional household information (e.g., demographic information,geographic location, etc.) from the Smart TV device 115 in the exampleSmart TV database 175. The example database interface 205 obtains thepanel data, return path data, and Smart TV data from the example paneldatabase 155, the example RPD database 165, and the example Smart TVdatabase 175, respectively. In some examples, the panel data, the returnpath data, and the Smart TV data are referred to as program tuning dataof households.

At block 1510, the example database interface 205 obtains referenceadvertisement data. In some examples, the example addressable ad datacollector 180 determines the reference advertisement data. The exampleaddressable ad data collector 180 collects the addressable advertisementdata from the example addressable ad provider 130 for monitoringaddressable advertisement exposure associated with media devices intarget households. The addressable ad data collector 180 stores theaddressable advertisement data as reference advertisement data alongwith additional household information (e.g., demographic information,geographic location, etc.) for the household selected by the addressablead provider 130 in the example addressable ad database 185. The databaseinterface 205 obtains the reference advertisement data from the exampleaddressable ad database 185. At block 1515, the example databaseinterface 205 combines panel data, RPD data, Smart TV data, andreference advertisement data. The database interface 205 combines thepanel data, the return path data, the Smart TV data, and the referenceadvertisement data. The database interface 205 analyzes the combinedpanel data, the return path data, the Smart TV data, and the referenceadvertisement data by identifying data associated with advertisementexposure (linear advertisements and addressable advertisements),removing duplicate data, etc.

At block 1520, the example advertisement determiner 210 identifiesrespondents that received addressable ad and respondents that receivelinear ad. In examples disclosed herein, a respondent may include ahousehold, an individual person, an individual media device, etc. Theadvertisement determiner 210 identifies the respondents that receivedaddressable advertisements and the respondents that receive linearadvertisements from the combined program tuning data and the referenceadvertisement data from the example database interface 205.

At block 1525, the example ad duration calculator 215 calculates addurations at the respondent-level. The ad duration calculator 215calculates the advertisement durations for linear advertisements and foraddressable advertisements. In some examples, the ad duration calculator215 determines the durations of a linear advertisement and anaddressable advertisement in seconds for each minute in a telecast. Forexample, during one minute of a telecast, the ad duration calculator 215determines that a linear advertisement was presented for 45 secondsduring the minute and an addressable advertisement was presented for 15seconds during the minute.

At block 1530, the example duration weighted impressions calculator 220calculates duration weighted impressions at respondent-level. Theduration weighted impressions calculator 220 calculates durationweighted impressions at the respondent-level based on the durations oflinear advertisements and the durations of addressable advertisementscalculated by the ad duration calculator 215. The duration weightedimpressions calculator 220 calculates the duration weighted impressionsfor the respondents. In examples disclosed herein impressions are thesums of weights of individual respondents that viewed the advertisementsduring a given minute. The duration weighted impressions calculator 220calculates the duration weighted impressions for the linearadvertisements and addressable advertisements using Equations 1a and 1b,as described above in connection with FIG. 2 . In examples disclosedherein, the duration weighted impressions account for weight valuesassociated with each respondent and the durations of the linearadvertisements and addressable advertisements calculated by the adduration calculator 215.

At block 1535, the example minute-level aggregator 225 aggregates theduration weighted impressions to the minute-level. The minute-levelaggregator 225 aggregates the duration weighted impressions for linearadvertisements and addressable advertisements for the respondents foreach commercial minute in the telecast. The minute-level aggregator 225sums the duration weighted impressions for each commercial minute usingthe Equations 2a and 2b, as described above in connection with FIG. 2 .The minute-level aggregator 225 determines the total commercial durationfor the telecast in seconds (e.g., across all commercial minutes). Theminute-level aggregator 225 determines the minute-level impressionsacross the telecast for both the linear advertisements and theaddressable advertisements by dividing the resulting total durationweighted impressions from Equations 2a, 2b above by the total commercialduration for the telecast.

At block 1540, the example commercial minute ratings calculator 230calculates the average commercial minute ratings. The commercial minuteratings calculator 230 calculates the average commercial minute ratingsbased on the minute-level duration weighted impressions from the exampleminute-level aggregator 225. The commercial minute ratings calculator230 calculates the average commercial minute rating for the addressableadvertisement and the average commercial minute rating for the linearadvertisement using the first total impressions of the addressableadvertisement, the second total impressions of the linear advertisement,and a total number of commercial seconds from the example minute-levelaggregator 225. The example commercial minute ratings calculator 230 maycalculate the average commercial minute ratings using Equations 3a and3b, as described above in connection with FIG. 2 . The commercial minuteratings calculator 230 determines the average commercial minutes whileaccounting for different advertisement durations at the respondent-levelto depict changes in an audience for linear advertisements andaddressable advertisements in a telecast.

At block 1545, the example communication interface 235 transmits theaverage commercial minute ratings. The communication interface 235transmits the average commercial minute ratings from the commercialminute ratings calculator 230 to the example ad ratings determiner 195of FIG. 1 . The example communication interface 235 transmits theaverage commercial minute ratings for the linear advertisements andaddressable advertisements for the ad ratings determiner 195 to creditthe addressable advertisement and the linear advertisement with audienceviewership metrics. In some examples, the ad ratings determiner 195 canuse the ratings data to select addressable advertisements forrespondents, modify the linear advertisements and addressableadvertisements, disable addressable advertisements for targetrespondents, etc. In some examples, the ad ratings determiner 195generates a report including data metrics regarding media exposureevents for advertisements during a telecast that may be presented tomedia providers and advertisers. After block 1545, the program 1500ends.

FIG. 16 is a block diagram of an example processor platform 1600structured to execute the instructions of FIG. 15 to implement theexample data center 145 and/or the example C3-C7 calculator 190 of FIGS.1 and 2 . The processor platform 1600 can be, for example, a server, apersonal computer, a workstation, a self-learning machine (e.g., aneural network), a mobile device (e.g., a cell phone, a smart phone, atablet such as an iPad), a personal digital assistant (PDA), an Internetappliance, a DVD player, a CD player, a digital video recorder, aBlu-ray player, a gaming console, a personal video recorder, a set topbox, a headset or other wearable device, or any other type of computingdevice.

The processor platform 1600 of the illustrated example includes aprocessor 1612. The processor 1612 of the illustrated example ishardware. For example, the processor 1612 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example meter data analyzer150, the example RPD collector 160, the example Smart TV data collector170, the example addressable ad data collector 180, the example adratings determiner 195, the example database interface 205, the exampleadvertisement determiner 210, the example ad duration calculator 215,the example duration weighted impressions calculator 220, the exampleminute-level aggregator 225, the example commercial minute ratingscalculator 230, the example communication interface 235.

The processor 1612 of the illustrated example includes a local memory1613 (e.g., a cache). The processor 1612 of the illustrated example isin communication with a main memory including a volatile memory 1614 anda non-volatile memory 1616 via a bus 1618. The volatile memory 1614 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1616 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1614,1616 is controlled by a memory controller.

The processor platform 1600 of the illustrated example also includes aninterface circuit 1620. The interface circuit 1620 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1622 are connectedto the interface circuit 1620. The input device(s) 1622 permit(s) a userto enter data and/or commands into the processor 1612. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1624 are also connected to the interfacecircuit 1620 of the illustrated example. The output devices 1624 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1620 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1620 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1626. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1600 of the illustrated example also includes oneor more mass storage devices 1628 for storing software and/or data.Examples of such mass storage devices 1628 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1632 of FIG. 15 may be stored in themass storage device 1628, in the volatile memory 1614, in thenon-volatile memory 1616, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that reconcilecommercial measurement ratings. The disclosed methods, apparatus andarticles of manufacture improve the C3-C7 metric for average commercialminutes to differentiate ratings for linear advertisements andaddressable advertisements. The disclosed methods, apparatus andarticles of manufacture collect program viewership data from householddevices and Smart TVs and return path data from service providers toidentify what advertisement devices were served. The disclosed methods,apparatus and articles of manufacture also obtain reference data fromadvertisers that indicate which devices were served the linearadvertisement during a time for that program broadcast, and whichdevices were served an addressable advertisement during that same timein the program broadcast. The disclosed methods, apparatus and articlesof manufacture use both the program viewership data collected and thereference advertisement data as inputs to the modified C3-C7 metric. Thedisclosed methods, apparatus and articles of manufacture weight ACMmeasurements from individual devices to determine the C3-C7 metrics fora program such that the C3-C7 distinguish between linear advertisementsand addressable advertisements.

Example methods, apparatus, systems, and articles of manufacture toreconcile commercial measurement ratings are disclosed herein. Furtherexamples and combinations thereof include the following:

Example 1 includes an apparatus comprising an advertisement determinerto identify a first plurality of respondents that received anaddressable advertisement and a second plurality of respondents thatreceived a linear advertisement based on combined program tuning dataand reference advertisement data, a calculator to calculate a firstaverage commercial minute rating for the addressable advertisement basedon first duration weighted impressions associated with the firstplurality of respondents and a second average commercial minute ratingfor the linear advertisement based on second duration weightedimpressions associated with the second plurality of respondents, and acommunication interface to transmit the first average commercial minuterating and the second average commercial minute rating for crediting theaddressable advertisement and the linear advertisement with audienceviewership metrics.

Example 2 includes the apparatus of example 1, wherein a respondent is ahousehold or an individual person.

Example 3 includes the apparatus of example 1, wherein the programtuning data includes at least panel data collected from media devices,return path data collected from service providers, and smart TV datacollected from smart television devices.

Example 4 includes the apparatus of example 1, wherein the calculator isa first calculator, and further including a second calculator tocalculate the first duration weighted impressions for the firstplurality of respondents and the second duration weighted impressionsthe second plurality of respondents, the first duration weightedimpressions and the second duration weighted impressions to account forweight values associated with each respondent included in the firstplurality of respondents and the second plurality of respondents.

Example 5 includes the apparatus of example 4, further including a thirdcalculator to calculate a first duration associated with the linearadvertisement and a second duration associated with the addressableadvertisement.

Example 6 includes the apparatus of example 5, wherein the secondcalculator is to calculate the first duration weighted impressions forthe first plurality of respondents and the second duration weightedimpressions for the second plurality of respondents based on the firstduration associated with the linear advertisement and the secondduration associated with the addressable advertisement.

Example 7 includes the apparatus of example 1, further including anaggregator to aggregate the first duration weighted impressions for thefirst plurality of respondents and aggregate the second durationweighted impressions for the second plurality of respondents for eachcommercial minute.

Example 8 includes the apparatus of example 7, wherein the calculator isto calculate a first total impressions of the addressable advertisementbased on a sum of the first duration weighted impressions for the firstplurality of respondents, and a second total impressions of the linearadvertisement based on a sum of the second duration weighted impressionsfor the second plurality of respondents.

Example 9 includes the apparatus of example 8, wherein the calculator isto calculate the first average commercial minute rating for theaddressable advertisement and the second average commercial minuterating for the linear advertisement based on the first total impressionsof the addressable advertisement, the second total impressions of thelinear advertisement, and a total number of commercial seconds.

Example 10 includes the apparatus of example 9, wherein the total numberof commercial seconds is a sum of a first duration associated with thelinear advertisement and a second duration associated with theaddressable advertisement.

Example 11 includes At least one non-transitory computer readable mediumcomprising instructions which, when executed, cause at least oneprocessor to at least identify a first plurality of respondents thatreceived an addressable advertisement and a second plurality ofrespondents that received a linear advertisement based on combinedprogram tuning data and reference advertisement data, calculate a firstaverage commercial minute rating for the addressable advertisement basedon first duration weighted impressions associated with the firstplurality of respondents and a second average commercial minute ratingfor the linear advertisement based on second duration weightedimpressions associated with the second plurality of respondents, andtransmit the first average commercial minute rating and the secondaverage commercial minute rating for crediting the addressableadvertisement and the linear advertisement with audience viewershipmetrics.

Example 12 includes the at least one non-transitory computer readablemedium of example 11, wherein a respondent is a household or anindividual person.

Example 13 includes the at least one non-transitory computer readablemedium of example 11, wherein the program tuning data includes at leastpanel data collected from media devices, return path data collected fromservice providers, and smart TV data collected from smart televisiondevices.

Example 14 includes the at least one non-transitory computer readablemedium of example 11, wherein the instructions cause the at least oneprocessor to calculate the first duration weighted impressions for thefirst plurality of respondents and second duration weighted impressionsfor the second plurality of respondents, the first duration weightedimpressions and the second duration weighted impressions to account forweight values associated with each respondent included in the firstplurality of respondents and the second plurality of respondents.

Example 15 includes the at least one non-transitory computer readablemedium of example 14, wherein the instructions cause the at least oneprocessor to calculate a first duration associated with the linearadvertisement and a second duration associated with the addressableadvertisement.

Example 16 includes the at least one non-transitory computer readablemedium of example 15, wherein the instructions cause the at least oneprocessor to calculate the first duration weighted impressions for thefirst plurality of respondents and the second duration weightedimpressions for the second plurality of respondents based on the firstduration associated with the linear advertisement and the secondduration associated with the addressable advertisement.

Example 17 includes the at least one non-transitory computer readablemedium of example 11, wherein the instructions cause the at least oneprocessor to aggregate the first duration weighted impressions for thefirst plurality of respondents and aggregate the second durationweighted impressions for the second plurality of respondents for eachcommercial minute.

Example 18 includes the at least one non-transitory computer readablemedium of example 17, wherein the instructions cause the at least oneprocessor to calculate a first total impressions of the addressableadvertisement based on a sum of the first duration weighted impressionsfor the first plurality of respondents, and a second total impressionsof the linear advertisement based on a sum of the second durationweighted impressions for the second plurality of respondents.

Example 19 includes the at least one non-transitory computer readablemedium of example 18, wherein the instructions cause the at least oneprocessor to calculate the first average commercial minute rating forthe addressable advertisement and the second average commercial minuterating for the linear advertisement based on the first total impressionsof the addressable advertisement, the second total impressions of thelinear advertisement, and a total number of commercial seconds.

Example 20 includes the at least one non-transitory computer readablemedium of example 19, wherein the total number of commercial seconds isa sum of a first duration associated with the linear advertisement and asecond duration associated with the addressable advertisement.

Example 21 includes a method comprising identifying a first plurality ofrespondents that received an addressable advertisement and a secondplurality of respondents that received a linear advertisement based oncombined program tuning data and reference advertisement data,calculating, by executing an instruction with at least one processor, afirst average commercial minute rating for the addressable advertisementbased on first duration weighted impressions associated with the firstplurality of respondents and a second average commercial minute ratingfor the linear advertisement based on second duration weightedimpressions associated with the second plurality of respondents, andtransmitting the first average commercial minute rating and the secondaverage commercial minute rating for crediting the addressableadvertisement and the linear advertisement with audience viewershipmetrics.

Example 22 includes the method of example 21, wherein a respondent is ahousehold or an individual person.

Example 23 includes the method of example 21, wherein the program tuningdata includes at least panel data collected from media devices, returnpath data collected from service providers, and smart TV data collectedfrom smart television devices.

Example 24 includes the method of example 21, further includingcalculating the first duration weighted impressions for the firstplurality of respondents and the second duration weighted impressionsfor the second plurality of respondents, the first duration weightedimpressions and the second duration weighted impressions to account forweight values associated with each respondent included in the firstplurality of respondents and the second plurality of respondents.

Example 25 includes the method of example 24, further includingcalculating a first duration associated with the linear advertisementand a second duration associated with the addressable advertisement.

Example 26 includes the method of example 25, further includingcalculating the first duration weighted impressions for the firstplurality of respondents and the second duration weighted impressionsfor the second plurality of respondents based on the first durationassociated with the linear advertisement and the second durationassociated with the addressable advertisement.

Example 27 includes the method of example 21, further includingaggregating the first duration weighted impressions for the firstplurality of respondents and aggregate the second duration weightedimpressions for the second plurality of respondents for each commercialminute.

Example 28 includes the method of example 27, further includingcalculating a first total impressions of the addressable advertisementbased on a sum of the first duration weighted impressions for the firstplurality of respondents, and a second total impressions of the linearadvertisement based on a sum of the second duration weighted impressionsfor the second plurality of respondents.

Example 29 includes the method of example 28, further includingcalculating the first average commercial minute rating for theaddressable advertisement and the second average commercial minuterating for the linear advertisement based on the first total impressionsof the addressable advertisement, the second total impressions of thelinear advertisement, and a total number of commercial seconds.

Example 30 includes the method of example 29, wherein the total numberof commercial seconds is a sum of a first duration associated with thelinear advertisement and a second duration associated with theaddressable advertisement.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: interface circuitry; computer readable instructions; and programmable circuitry to at least one of execute or instantiate the computer readable instructions to: communicate with one or more meters and one or more media devices to obtain combined program tuning data associated with a plurality of media devices, the plurality of media devices including the one or more media devices; communicate with an addressable advertisement provider to obtain reference advertisement data associated with addressable advertisements provided to the one or more media devices; identify a first plurality of respondents that received an addressable advertisement during a commercial time period of a media program on ones of the plurality of media devices targeted by the addressable advertisement based on the combined program tuning data and reference advertisement data, the ones of the plurality of media devices associated with ones of the first plurality of respondents; identify a second plurality of respondents that received a linear advertisement during the commercial time period of the media program based on the combined program tuning data and the reference advertisement data, the linear advertisement broadcast with the media program and not targeted to a particular one of the plurality of media devices; calculate a first average commercial minute rating for addressable advertisements during the media program based on first duration weighted impressions associated with the first plurality of respondents; calculate a second average commercial minute rating for linear advertisements during the media program based on second duration weighted impressions associated with the second plurality of respondents; disable addressable advertisement delivery for one or more respondents based on the first average commercial minute rating and the second average commercial minute rating; and transmit, via an electronic communication network, the first average commercial minute rating and the second average commercial minute rating to a computer database, the first average commercial minute rating and the second average commercial minute rating to adjust a computer-calculated average commercial rating metric to differentiate crediting between the addressable advertisement and the linear advertisement.
 2. The apparatus of claim 1, wherein a respondent is a household or an individual person.
 3. The apparatus of claim 1, wherein the combined program tuning data includes at least panel data collected from media devices, return path data collected from service providers, and smart television data collected from smart television devices.
 4. The apparatus of claim 1, wherein the programmable circuitry is to calculate the first duration weighted impressions for the first plurality of respondents and the second duration weighted impressions the second plurality of respondents, the first duration weighted impressions and the second duration weighted impressions to account for weight values associated with each respondent included in the first plurality of respondents and the second plurality of respondents.
 5. The apparatus of claim 4, wherein the programmable circuitry to calculate a first duration associated with the linear advertisement and a second duration associated with the addressable advertisement.
 6. The apparatus of claim 5, wherein the programmable circuitry is to calculate the first duration weighted impressions for the first plurality of respondents and the second duration weighted impressions for the second plurality of respondents based on the first duration associated with the linear advertisement and the second duration associated with the addressable advertisement.
 7. The apparatus of claim 1, wherein the programmable circuitry is to aggregate the first duration weighted impressions for the first plurality of respondents and aggregate the second duration weighted impressions for the second plurality of respondents for each commercial minute.
 8. The apparatus of claim 7, wherein the programmable circuitry is to calculate a first total impressions of the addressable advertisement based on a sum of the first duration weighted impressions for the first plurality of respondents, and a second total impressions of the linear advertisement based on a sum of the second duration weighted impressions for the second plurality of respondents.
 9. The apparatus of claim 8, wherein the programmable circuitry is to calculate the first average commercial minute rating for the addressable advertisement and the second average commercial minute rating for the linear advertisement based on the first total impressions of the addressable advertisement, the second total impressions of the linear advertisement, and a total number of commercial seconds.
 10. The apparatus of claim 9, wherein the total number of commercial seconds is a sum of a first duration associated with the linear advertisement and a second duration associated with the addressable advertisement.
 11. At least one non-transitory computer readable medium comprising instructions which, when executed, cause at least one processor to at least: communicate with one or more meters and one or more media devices to obtain combined program tuning data associated with a plurality of media devices, the plurality of media devices including the one or more media devices; communicate with an addressable advertisement provider to obtain reference advertisement data associated with addressable advertisements provided to the one or more media devices; identify a first plurality of respondents that received an addressable advertisement during a commercial time period of a media program on ones of the plurality of media devices targeted by the addressable advertisement based on the combined program tuning data and reference advertisement data, the ones of the plurality of media devices associated with ones of the first plurality of respondents; identify a second plurality of respondents that received a linear advertisement during the commercial time period of the media program based on the combined program tuning data and the reference advertisement data, the linear advertisement broadcast with the media program and not targeted to a particular one of the plurality of media devices; calculate a first average commercial minute rating for addressable advertisements during the media program based on first duration weighted impressions associated with the first plurality of respondents; calculate a second average commercial minute rating for linear advertisements during the media program based on second duration weighted impressions associated with the second plurality of respondents; disable addressable advertisement delivery for one or more respondents based on the first average commercial minute rating and the second average commercial minute rating; and transmit, via an electronic communication network, the first average commercial minute rating and the second average commercial minute rating to a computer database, the first average commercial minute rating and the second average commercial minute rating to adjust a computer-calculated average commercial rating metric to differentiate crediting between the addressable advertisement and the linear advertisement.
 12. The at least one non-transitory computer readable medium of claim 11, wherein the instructions cause the at least one processor to calculate the first duration weighted impressions for the first plurality of respondents and second duration weighted impressions for the second plurality of respondents, the first duration weighted impressions and the second duration weighted impressions to account for weight values associated with each respondent included in the first plurality of respondents and the second plurality of respondents.
 13. The at least one non-transitory computer readable medium of claim 12, wherein the instructions cause the at least one processor to calculate a first duration associated with the linear advertisement and a second duration associated with the addressable advertisement.
 14. The at least one non-transitory computer readable medium of claim 13, wherein the instructions cause the at least one processor to calculate the first duration weighted impressions for the first plurality of respondents and the second duration weighted impressions for the second plurality of respondents based on the first duration associated with the linear advertisement and the second duration associated with the addressable advertisement.
 15. The at least one non-transitory computer readable medium of claim 11, wherein the instructions cause the at least one processor to aggregate the first duration weighted impressions for the first plurality of respondents and aggregate the second duration weighted impressions for the second plurality of respondents for each commercial minute.
 16. The at least one non-transitory computer readable medium of claim 15, wherein the instructions cause the at least one processor to calculate a first total impressions of the addressable advertisement based on a sum of the first duration weighted impressions for the first plurality of respondents, and a second total impressions of the linear advertisement based on a sum of the second duration weighted impressions for the second plurality of respondents.
 17. The at least one non-transitory computer readable medium of claim 16, wherein the instructions cause the at least one processor to calculate the first average commercial minute rating for the addressable advertisement and the second average commercial minute rating for the linear advertisement based on the first total impressions of the addressable advertisement, the second total impressions of the linear advertisement, and a total number of commercial seconds.
 18. The at least one non-transitory computer readable medium of claim 17, wherein the total number of commercial seconds is a sum of a first duration associated with the linear advertisement and a second duration associated with the addressable advertisement.
 19. A method comprising: communicating with one or more meters and one or more media devices to obtain combined program tuning data associated with a plurality of media devices, the plurality of media devices including the one or more media devices; communicating with an addressable advertisement provider to obtain reference advertisement data associated with addressable advertisements provided to the one or more media devices; identifying a first plurality of respondents that received an addressable advertisement during a commercial time period of a media program on ones of the plurality of media devices targeted by the addressable advertisement based on the combined program tuning data and reference advertisement data, the ones of the plurality of media devices associated with ones of the first plurality of respondents; identifying a second plurality of respondents that received a linear advertisement during the commercial time period of the media program based on the combined program tuning data and the reference advertisement data, the linear advertisement broadcast with the media program and not targeted to a particular one of the plurality of media devices; calculating a first average commercial minute rating for addressable advertisements during the media program based on first duration weighted impressions associated with the first plurality of respondents, calculating a second average commercial minute rating for linear advertisements during the media program based on second duration weighted impressions associated with the second plurality of respondents; disabling addressable advertisement delivery for one or more respondents based on the first average commercial minute rating and the second average commercial minute rating; and transmitting, via an electronic communication network, the first average commercial minute rating and the second average commercial minute rating to a computer database, the first average commercial minute rating and the second average commercial minute rating to adjust a computer-calculated average commercial rating metric to differentiate crediting between the addressable advertisement and the linear advertisement.
 20. The method of claim 19, wherein the combined program tuning data includes at least panel data collected from media devices, return path data collected from service providers, and smart TV data collected from smart television devices. 